Overview

Dataset statistics

Number of variables20
Number of observations113468
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.4 MiB
Average record size in memory161.0 B

Variable types

Text5
Numeric12
Boolean1
Categorical2

Alerts

energia is highly overall correlated with volume and 1 other fieldsHigh correlation
volume is highly overall correlated with energia and 1 other fieldsHigh correlation
acustica is highly overall correlated with energia and 1 other fieldsHigh correlation
explicito is highly imbalanced (57.8%)Imbalance
assinatura_hora is highly imbalanced (74.0%)Imbalance
popularidade has 15830 (14.0%) zerosZeros
chave has 13000 (11.5%) zerosZeros
instrumentalidade has 38621 (34.0%) zerosZeros

Reproduction

Analysis started2023-09-21 21:00:41.737371
Analysis finished2023-09-21 21:01:34.674594
Duration52.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct89740
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:35.093710image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2496296
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73461 ?
Unique (%)64.7%

Sample

1st row5SuOikwiRyPMVoIQDJUgSV
2nd row4qPNDBW1i3p13qLCt0Ki3A
3rd row1iJBSr7s7jYXzM8EGcbK5b
4th row6lfxq3CG4xtTiEg7opyCyx
5th row5vjLSffimiIP26QG5WcN2K
ValueCountFrequency (%)
6s3jldagk3uu3ntzbpnuhs 9
 
< 0.1%
2kkvb3rnrzwjfdghaua0tz 8
 
< 0.1%
2ey6v4sekh3z0rusisrosd 8
 
< 0.1%
5ftfvzslii5zxydnbrtf41 7
 
< 0.1%
4wjtkbnjq41zxnb84jswaj 7
 
< 0.1%
1gqpa08t7ebavpqj9o9l2q 7
 
< 0.1%
5zsahuq24mwhiduaxjqnhw 7
 
< 0.1%
7tbzfr8zvzzjezy6v0d6el 7
 
< 0.1%
5sqkarfxe7uejhtlcthcls 7
 
< 0.1%
6bzwr3epseolvwlblk58il 7
 
< 0.1%
Other values (89730) 113394
99.9%
2023-09-21T18:01:35.789377image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 53532
 
2.1%
5 53247
 
2.1%
2 53086
 
2.1%
6 53017
 
2.1%
0 52954
 
2.1%
1 52940
 
2.1%
4 52911
 
2.1%
7 50287
 
2.0%
K 39025
 
1.6%
D 38920
 
1.6%
Other values (52) 1996377
80.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 998630
40.0%
Uppercase Letter 998337
40.0%
Decimal Number 499329
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 39025
 
3.9%
D 38920
 
3.9%
A 38809
 
3.9%
G 38801
 
3.9%
W 38578
 
3.9%
E 38571
 
3.9%
X 38533
 
3.9%
B 38530
 
3.9%
I 38507
 
3.9%
R 38506
 
3.9%
Other values (16) 611557
61.3%
Lowercase Letter
ValueCountFrequency (%)
k 38885
 
3.9%
f 38784
 
3.9%
h 38753
 
3.9%
l 38735
 
3.9%
y 38702
 
3.9%
e 38686
 
3.9%
p 38613
 
3.9%
i 38589
 
3.9%
b 38536
 
3.9%
c 38484
 
3.9%
Other values (16) 611863
61.3%
Decimal Number
ValueCountFrequency (%)
3 53532
10.7%
5 53247
10.7%
2 53086
10.6%
6 53017
10.6%
0 52954
10.6%
1 52940
10.6%
4 52911
10.6%
7 50287
10.1%
8 38909
7.8%
9 38446
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1996967
80.0%
Common 499329
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 39025
 
2.0%
D 38920
 
1.9%
k 38885
 
1.9%
A 38809
 
1.9%
G 38801
 
1.9%
f 38784
 
1.9%
h 38753
 
1.9%
l 38735
 
1.9%
y 38702
 
1.9%
e 38686
 
1.9%
Other values (42) 1608867
80.6%
Common
ValueCountFrequency (%)
3 53532
10.7%
5 53247
10.7%
2 53086
10.6%
6 53017
10.6%
0 52954
10.6%
1 52940
10.6%
4 52911
10.6%
7 50287
10.1%
8 38909
7.8%
9 38446
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2496296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 53532
 
2.1%
5 53247
 
2.1%
2 53086
 
2.1%
6 53017
 
2.1%
0 52954
 
2.1%
1 52940
 
2.1%
4 52911
 
2.1%
7 50287
 
2.0%
K 39025
 
1.6%
D 38920
 
1.6%
Other values (52) 1996377
80.0%
Distinct31437
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:36.382838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length513
Median length322
Mean length16.284459
Min length2

Characters and Unicode

Total characters1847765
Distinct characters712
Distinct categories18 ?
Distinct scripts7 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16814 ?
Unique (%)14.8%

Sample

1st rowGen Hoshino
2nd rowBen Woodward
3rd rowIngrid Michaelson;ZAYN
4th rowKina Grannis
5th rowChord Overstreet
ValueCountFrequency (%)
the 6808
 
2.7%
3116
 
1.2%
de 1131
 
0.4%
los 1066
 
0.4%
of 1026
 
0.4%
dj 706
 
0.3%
george 582
 
0.2%
la 518
 
0.2%
jones 511
 
0.2%
for 456
 
0.2%
Other values (42276) 240428
93.8%
2023-09-21T18:01:37.218833image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 163119
 
8.8%
e 147786
 
8.0%
142888
 
7.7%
i 111438
 
6.0%
n 105807
 
5.7%
o 103245
 
5.6%
r 99578
 
5.4%
l 75186
 
4.1%
s 68932
 
3.7%
t 63252
 
3.4%
Other values (702) 766534
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1304267
70.6%
Uppercase Letter 336413
 
18.2%
Space Separator 142888
 
7.7%
Other Punctuation 53454
 
2.9%
Decimal Number 5623
 
0.3%
Dash Punctuation 2081
 
0.1%
Other Letter 2008
 
0.1%
Currency Symbol 286
 
< 0.1%
Close Punctuation 181
 
< 0.1%
Open Punctuation 179
 
< 0.1%
Other values (8) 385
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
63
 
3.1%
59
 
2.9%
56
 
2.8%
49
 
2.4%
43
 
2.1%
42
 
2.1%
41
 
2.0%
41
 
2.0%
33
 
1.6%
26
 
1.3%
Other values (453) 1555
77.4%
Lowercase Letter
ValueCountFrequency (%)
a 163119
12.5%
e 147786
11.3%
i 111438
 
8.5%
n 105807
 
8.1%
o 103245
 
7.9%
r 99578
 
7.6%
l 75186
 
5.8%
s 68932
 
5.3%
t 63252
 
4.8%
h 50873
 
3.9%
Other values (102) 315051
24.2%
Uppercase Letter
ValueCountFrequency (%)
S 28975
 
8.6%
A 23825
 
7.1%
M 23811
 
7.1%
B 22735
 
6.8%
T 20601
 
6.1%
C 20200
 
6.0%
D 18406
 
5.5%
R 17470
 
5.2%
L 16591
 
4.9%
P 15597
 
4.6%
Other values (66) 128202
38.1%
Other Punctuation
ValueCountFrequency (%)
; 43880
82.1%
. 3763
 
7.0%
& 2982
 
5.6%
' 1306
 
2.4%
" 548
 
1.0%
! 307
 
0.6%
, 285
 
0.5%
/ 161
 
0.3%
: 154
 
0.3%
? 32
 
0.1%
Other values (9) 36
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 883
15.7%
2 816
14.5%
4 704
12.5%
3 682
12.1%
0 533
9.5%
7 452
8.0%
8 440
7.8%
6 414
7.4%
9 376
6.7%
5 323
 
5.7%
Close Punctuation
ValueCountFrequency (%)
) 147
81.2%
] 24
 
13.3%
5
 
2.8%
3
 
1.7%
} 2
 
1.1%
Math Symbol
ValueCountFrequency (%)
+ 82
52.9%
= 48
31.0%
23
 
14.8%
| 1
 
0.6%
1
 
0.6%
Other Symbol
ValueCountFrequency (%)
6
37.5%
5
31.2%
2
 
12.5%
® 2
 
12.5%
1
 
6.2%
Open Punctuation
ValueCountFrequency (%)
( 147
82.1%
[ 24
 
13.4%
5
 
2.8%
3
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 1988
95.5%
93
 
4.5%
Modifier Letter
ValueCountFrequency (%)
117
98.3%
2
 
1.7%
Final Punctuation
ValueCountFrequency (%)
49
98.0%
1
 
2.0%
Initial Punctuation
ValueCountFrequency (%)
4
80.0%
1
 
20.0%
Space Separator
ValueCountFrequency (%)
142888
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 286
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 20
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 11
100.0%
Other Number
ValueCountFrequency (%)
² 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1634882
88.5%
Common 205082
 
11.1%
Cyrillic 5788
 
0.3%
Han 1291
 
0.1%
Katakana 622
 
< 0.1%
Hiragana 97
 
< 0.1%
Greek 3
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
33
 
2.6%
26
 
2.0%
23
 
1.8%
20
 
1.5%
19
 
1.5%
19
 
1.5%
18
 
1.4%
18
 
1.4%
18
 
1.4%
17
 
1.3%
Other values (380) 1080
83.7%
Latin
ValueCountFrequency (%)
a 163119
 
10.0%
e 147786
 
9.0%
i 111438
 
6.8%
n 105807
 
6.5%
o 103245
 
6.3%
r 99578
 
6.1%
l 75186
 
4.6%
s 68932
 
4.2%
t 63252
 
3.9%
h 50873
 
3.1%
Other values (120) 645666
39.5%
Common
ValueCountFrequency (%)
142888
69.7%
; 43880
 
21.4%
. 3763
 
1.8%
& 2982
 
1.5%
- 1988
 
1.0%
' 1306
 
0.6%
1 883
 
0.4%
2 816
 
0.4%
4 704
 
0.3%
3 682
 
0.3%
Other values (51) 5190
 
2.5%
Cyrillic
ValueCountFrequency (%)
а 723
 
12.5%
о 436
 
7.5%
р 412
 
7.1%
и 388
 
6.7%
е 352
 
6.1%
н 338
 
5.8%
к 250
 
4.3%
в 246
 
4.3%
л 232
 
4.0%
с 200
 
3.5%
Other values (45) 2211
38.2%
Katakana
ValueCountFrequency (%)
63
 
10.1%
59
 
9.5%
56
 
9.0%
49
 
7.9%
43
 
6.9%
42
 
6.8%
41
 
6.6%
41
 
6.6%
19
 
3.1%
18
 
2.9%
Other values (35) 191
30.7%
Hiragana
ValueCountFrequency (%)
10
 
10.3%
8
 
8.2%
7
 
7.2%
6
 
6.2%
6
 
6.2%
6
 
6.2%
6
 
6.2%
5
 
5.2%
5
 
5.2%
4
 
4.1%
Other values (19) 34
35.1%
Greek
ValueCountFrequency (%)
α 2
66.7%
μ 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1830296
99.1%
None 9363
 
0.5%
Cyrillic 5788
 
0.3%
CJK 1289
 
0.1%
Katakana 740
 
< 0.1%
Punctuation 149
 
< 0.1%
Hiragana 97
 
< 0.1%
Math Operators 24
 
< 0.1%
Misc Symbols 6
 
< 0.1%
Letterlike Symbols 5
 
< 0.1%
Other values (2) 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 163119
 
8.9%
e 147786
 
8.1%
142888
 
7.8%
i 111438
 
6.1%
n 105807
 
5.8%
o 103245
 
5.6%
r 99578
 
5.4%
l 75186
 
4.1%
s 68932
 
3.8%
t 63252
 
3.5%
Other values (78) 749065
40.9%
None
ValueCountFrequency (%)
é 1490
15.9%
ã 885
 
9.5%
á 718
 
7.7%
ö 693
 
7.4%
ó 587
 
6.3%
í 570
 
6.1%
ü 498
 
5.3%
ä 419
 
4.5%
ç 362
 
3.9%
ë 294
 
3.1%
Other values (80) 2847
30.4%
Cyrillic
ValueCountFrequency (%)
а 723
 
12.5%
о 436
 
7.5%
р 412
 
7.1%
и 388
 
6.7%
е 352
 
6.1%
н 338
 
5.8%
к 250
 
4.3%
в 246
 
4.3%
л 232
 
4.0%
с 200
 
3.5%
Other values (45) 2211
38.2%
Katakana
ValueCountFrequency (%)
117
15.8%
63
 
8.5%
59
 
8.0%
56
 
7.6%
49
 
6.6%
43
 
5.8%
42
 
5.7%
41
 
5.5%
41
 
5.5%
19
 
2.6%
Other values (37) 210
28.4%
Punctuation
ValueCountFrequency (%)
93
62.4%
49
32.9%
4
 
2.7%
1
 
0.7%
1
 
0.7%
1
 
0.7%
CJK
ValueCountFrequency (%)
33
 
2.6%
26
 
2.0%
23
 
1.8%
20
 
1.6%
19
 
1.5%
19
 
1.5%
18
 
1.4%
18
 
1.4%
18
 
1.4%
17
 
1.3%
Other values (379) 1078
83.6%
Math Operators
ValueCountFrequency (%)
23
95.8%
1
 
4.2%
Hiragana
ValueCountFrequency (%)
10
 
10.3%
8
 
8.2%
7
 
7.2%
6
 
6.2%
6
 
6.2%
6
 
6.2%
6
 
6.2%
5
 
5.2%
5
 
5.2%
4
 
4.1%
Other values (19) 34
35.1%
Misc Symbols
ValueCountFrequency (%)
6
100.0%
Letterlike Symbols
ValueCountFrequency (%)
5
100.0%
Latin Ext Additional
ValueCountFrequency (%)
3
60.0%
2
40.0%
Dingbats
ValueCountFrequency (%)
2
66.7%
1
33.3%
Distinct46589
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:37.779833image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length243
Median length145
Mean length20.097772
Min length1

Characters and Unicode

Total characters2280454
Distinct characters2084
Distinct categories22 ?
Distinct scripts13 ?
Distinct blocks22 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28035 ?
Unique (%)24.7%

Sample

1st rowComedy
2nd rowGhost (Acoustic)
3rd rowTo Begin Again
4th rowCrazy Rich Asians (Original Motion Picture Soundtrack)
5th rowHold On
ValueCountFrequency (%)
the 11977
 
3.1%
9111
 
2.3%
of 5206
 
1.3%
2022 3414
 
0.9%
vol 3243
 
0.8%
christmas 3202
 
0.8%
vivo 3185
 
0.8%
a 3145
 
0.8%
ao 2928
 
0.8%
de 2883
 
0.7%
Other values (35981) 341366
87.6%
2023-09-21T18:01:38.623335image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
276192
 
12.1%
e 184181
 
8.1%
a 142113
 
6.2%
o 137781
 
6.0%
i 127050
 
5.6%
n 105672
 
4.6%
r 105362
 
4.6%
s 96110
 
4.2%
t 95959
 
4.2%
l 78622
 
3.4%
Other values (2074) 931412
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1490738
65.4%
Uppercase Letter 363192
 
15.9%
Space Separator 276192
 
12.1%
Decimal Number 50127
 
2.2%
Other Punctuation 32563
 
1.4%
Other Letter 21996
 
1.0%
Close Punctuation 18287
 
0.8%
Open Punctuation 18285
 
0.8%
Dash Punctuation 7169
 
0.3%
Math Symbol 828
 
< 0.1%
Other values (12) 1077
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
399
 
1.8%
375
 
1.7%
288
 
1.3%
287
 
1.3%
255
 
1.2%
233
 
1.1%
222
 
1.0%
210
 
1.0%
193
 
0.9%
179
 
0.8%
Other values (1728) 19355
88.0%
Lowercase Letter
ValueCountFrequency (%)
e 184181
12.4%
a 142113
 
9.5%
o 137781
 
9.2%
i 127050
 
8.5%
n 105672
 
7.1%
r 105362
 
7.1%
s 96110
 
6.4%
t 95959
 
6.4%
l 78622
 
5.3%
u 52119
 
3.5%
Other values (128) 365769
24.5%
Uppercase Letter
ValueCountFrequency (%)
S 30105
 
8.3%
T 27763
 
7.6%
A 26069
 
7.2%
M 22567
 
6.2%
C 22239
 
6.1%
P 19666
 
5.4%
R 18974
 
5.2%
B 17997
 
5.0%
E 17944
 
4.9%
D 17673
 
4.9%
Other values (91) 142195
39.2%
Other Punctuation
ValueCountFrequency (%)
. 9127
28.0%
' 5165
15.9%
, 4843
14.9%
: 4386
13.5%
& 2535
 
7.8%
/ 2100
 
6.4%
" 1604
 
4.9%
! 1243
 
3.8%
? 500
 
1.5%
# 247
 
0.8%
Other values (13) 813
 
2.5%
Nonspacing Mark
ValueCountFrequency (%)
57
52.3%
́ 12
 
11.0%
̆ 11
 
10.1%
8
 
7.3%
̈ 6
 
5.5%
4
 
3.7%
3
 
2.8%
2
 
1.8%
̀ 2
 
1.8%
1
 
0.9%
Other values (3) 3
 
2.8%
Decimal Number
ValueCountFrequency (%)
2 18718
37.3%
0 12424
24.8%
1 6960
 
13.9%
9 2611
 
5.2%
3 2405
 
4.8%
5 1886
 
3.8%
4 1464
 
2.9%
7 1292
 
2.6%
6 1209
 
2.4%
8 1158
 
2.3%
Math Symbol
ValueCountFrequency (%)
+ 299
36.1%
~ 271
32.7%
| 84
 
10.1%
> 69
 
8.3%
< 66
 
8.0%
= 19
 
2.3%
10
 
1.2%
× 7
 
0.8%
÷ 2
 
0.2%
1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 17309
94.7%
[ 810
 
4.4%
53
 
0.3%
51
 
0.3%
43
 
0.2%
10
 
0.1%
{ 8
 
< 0.1%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 17308
94.6%
] 813
 
4.4%
53
 
0.3%
51
 
0.3%
43
 
0.2%
10
 
0.1%
} 8
 
< 0.1%
1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
® 20
32.8%
° 17
27.9%
14
23.0%
4
 
6.6%
2
 
3.3%
2
 
3.3%
1
 
1.6%
1
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 6914
96.4%
185
 
2.6%
59
 
0.8%
6
 
0.1%
4
 
0.1%
1
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 30
90.9%
` 2
 
6.1%
˚ 1
 
3.0%
Letter Number
ValueCountFrequency (%)
15
75.0%
4
 
20.0%
1
 
5.0%
Spacing Mark
ValueCountFrequency (%)
5
55.6%
3
33.3%
1
 
11.1%
Modifier Letter
ValueCountFrequency (%)
396
95.2%
20
 
4.8%
Final Punctuation
ValueCountFrequency (%)
203
81.5%
46
 
18.5%
Initial Punctuation
ValueCountFrequency (%)
54
90.0%
6
 
10.0%
Format
ValueCountFrequency (%)
14
87.5%
2
 
12.5%
Space Separator
ValueCountFrequency (%)
276192
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 72
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 31
100.0%
Other Number
ValueCountFrequency (%)
² 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1836583
80.5%
Common 404370
 
17.7%
Cyrillic 17300
 
0.8%
Han 13300
 
0.6%
Katakana 5077
 
0.2%
Hiragana 3504
 
0.2%
Inherited 100
 
< 0.1%
Greek 80
 
< 0.1%
Hangul 47
 
< 0.1%
Arabic 44
 
< 0.1%
Other values (3) 49
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
288
 
2.2%
233
 
1.8%
193
 
1.5%
134
 
1.0%
132
 
1.0%
132
 
1.0%
124
 
0.9%
116
 
0.9%
110
 
0.8%
109
 
0.8%
Other values (1537) 11729
88.2%
Latin
ValueCountFrequency (%)
e 184181
 
10.0%
a 142113
 
7.7%
o 137781
 
7.5%
i 127050
 
6.9%
n 105672
 
5.8%
r 105362
 
5.7%
s 96110
 
5.2%
t 95959
 
5.2%
l 78622
 
4.3%
u 52119
 
2.8%
Other values (144) 711614
38.7%
Common
ValueCountFrequency (%)
276192
68.3%
2 18718
 
4.6%
( 17309
 
4.3%
) 17308
 
4.3%
0 12424
 
3.1%
. 9127
 
2.3%
1 6960
 
1.7%
- 6914
 
1.7%
' 5165
 
1.3%
, 4843
 
1.2%
Other values (77) 29410
 
7.3%
Katakana
ValueCountFrequency (%)
399
 
7.9%
287
 
5.7%
255
 
5.0%
210
 
4.1%
179
 
3.5%
169
 
3.3%
166
 
3.3%
160
 
3.2%
151
 
3.0%
145
 
2.9%
Other values (68) 2956
58.2%
Hiragana
ValueCountFrequency (%)
375
 
10.7%
222
 
6.3%
173
 
4.9%
153
 
4.4%
143
 
4.1%
134
 
3.8%
127
 
3.6%
117
 
3.3%
110
 
3.1%
106
 
3.0%
Other values (60) 1844
52.6%
Cyrillic
ValueCountFrequency (%)
е 1469
 
8.5%
а 1454
 
8.4%
о 1338
 
7.7%
н 1194
 
6.9%
с 1164
 
6.7%
и 1132
 
6.5%
т 830
 
4.8%
р 818
 
4.7%
л 681
 
3.9%
к 674
 
3.9%
Other values (53) 6546
37.8%
Greek
ValueCountFrequency (%)
φ 27
33.8%
α 6
 
7.5%
Ξ 5
 
6.2%
ς 4
 
5.0%
μ 3
 
3.8%
Ψ 3
 
3.8%
ι 2
 
2.5%
ο 2
 
2.5%
Τ 2
 
2.5%
ν 2
 
2.5%
Other values (16) 24
30.0%
Malayalam
ValueCountFrequency (%)
5
13.2%
4
10.5%
4
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
Other values (8) 8
21.1%
Hangul
ValueCountFrequency (%)
8
17.0%
6
12.8%
4
8.5%
4
8.5%
4
8.5%
4
8.5%
4
8.5%
2
 
4.3%
2
 
4.3%
2
 
4.3%
Other values (6) 7
14.9%
Arabic
ValueCountFrequency (%)
ا 8
18.2%
ی 6
13.6%
چ 4
9.1%
ت 4
9.1%
ر 4
9.1%
و 4
9.1%
ه 4
9.1%
ک 4
9.1%
ل 4
9.1%
م 2
 
4.5%
Inherited
ValueCountFrequency (%)
57
57.0%
́ 12
 
12.0%
̆ 11
 
11.0%
8
 
8.0%
̈ 6
 
6.0%
2
 
2.0%
̀ 2
 
2.0%
̃ 1
 
1.0%
̊ 1
 
1.0%
Thai
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Devanagari
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2225388
97.6%
Cyrillic 17300
 
0.8%
None 14493
 
0.6%
CJK 13279
 
0.6%
Katakana 5702
 
0.3%
Hiragana 3569
 
0.2%
Punctuation 450
 
< 0.1%
Hangul 47
 
< 0.1%
Arabic 44
 
< 0.1%
Malayalam 38
 
< 0.1%
Other values (12) 144
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
276192
 
12.4%
e 184181
 
8.3%
a 142113
 
6.4%
o 137781
 
6.2%
i 127050
 
5.7%
n 105672
 
4.7%
r 105362
 
4.7%
s 96110
 
4.3%
t 95959
 
4.3%
l 78622
 
3.5%
Other values (83) 876346
39.4%
Cyrillic
ValueCountFrequency (%)
е 1469
 
8.5%
а 1454
 
8.4%
о 1338
 
7.7%
н 1194
 
6.9%
с 1164
 
6.7%
и 1132
 
6.5%
т 830
 
4.8%
р 818
 
4.7%
л 681
 
3.9%
к 674
 
3.9%
Other values (53) 6546
37.8%
None
ValueCountFrequency (%)
ó 1193
 
8.2%
ã 1160
 
8.0%
á 1145
 
7.9%
é 1067
 
7.4%
ç 844
 
5.8%
ú 836
 
5.8%
ñ 739
 
5.1%
ü 725
 
5.0%
í 717
 
4.9%
ı 672
 
4.6%
Other values (132) 5395
37.2%
Katakana
ValueCountFrequency (%)
399
 
7.0%
396
 
6.9%
287
 
5.0%
255
 
4.5%
229
 
4.0%
210
 
3.7%
179
 
3.1%
169
 
3.0%
166
 
2.9%
160
 
2.8%
Other values (70) 3252
57.0%
Hiragana
ValueCountFrequency (%)
375
 
10.5%
222
 
6.2%
173
 
4.8%
153
 
4.3%
143
 
4.0%
134
 
3.8%
127
 
3.6%
117
 
3.3%
110
 
3.1%
106
 
3.0%
Other values (62) 1909
53.5%
CJK
ValueCountFrequency (%)
288
 
2.2%
233
 
1.8%
193
 
1.5%
134
 
1.0%
132
 
1.0%
132
 
1.0%
124
 
0.9%
116
 
0.9%
110
 
0.8%
109
 
0.8%
Other values (1535) 11708
88.2%
Punctuation
ValueCountFrequency (%)
203
45.1%
59
 
13.1%
54
 
12.0%
46
 
10.2%
46
 
10.2%
14
 
3.1%
6
 
1.3%
6
 
1.3%
6
 
1.3%
4
 
0.9%
Other values (3) 6
 
1.3%
IPA Ext
ValueCountFrequency (%)
ə 20
100.0%
Number Forms
ValueCountFrequency (%)
15
78.9%
4
 
21.1%
Misc Symbols
ValueCountFrequency (%)
14
70.0%
4
 
20.0%
2
 
10.0%
Diacriticals
ValueCountFrequency (%)
́ 12
36.4%
̆ 11
33.3%
̈ 6
18.2%
̀ 2
 
6.1%
̃ 1
 
3.0%
̊ 1
 
3.0%
Math Operators
ValueCountFrequency (%)
10
90.9%
1
 
9.1%
Hangul
ValueCountFrequency (%)
8
17.0%
6
12.8%
4
8.5%
4
8.5%
4
8.5%
4
8.5%
4
8.5%
2
 
4.3%
2
 
4.3%
2
 
4.3%
Other values (6) 7
14.9%
Arabic
ValueCountFrequency (%)
ا 8
18.2%
ی 6
13.6%
چ 4
9.1%
ت 4
9.1%
ر 4
9.1%
و 4
9.1%
ه 4
9.1%
ک 4
9.1%
ل 4
9.1%
م 2
 
4.5%
Devanagari
ValueCountFrequency (%)
6
100.0%
Latin Ext Additional
ValueCountFrequency (%)
6
26.1%
6
26.1%
3
13.0%
2
 
8.7%
2
 
8.7%
2
 
8.7%
2
 
8.7%
Malayalam
ValueCountFrequency (%)
5
13.2%
4
10.5%
4
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
Other values (8) 8
21.1%
Dingbats
ValueCountFrequency (%)
2
66.7%
1
33.3%
VS
ValueCountFrequency (%)
2
100.0%
Geometric Shapes
ValueCountFrequency (%)
1
100.0%
Thai
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Modifier Letters
ValueCountFrequency (%)
˚ 1
100.0%
Distinct73608
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:39.206962image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length511
Median length146
Mean length17.961258
Min length1

Characters and Unicode

Total characters2038028
Distinct characters2417
Distinct categories23 ?
Distinct scripts13 ?
Distinct blocks25 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55979 ?
Unique (%)49.3%

Sample

1st rowComedy
2nd rowGhost - Acoustic
3rd rowTo Begin Again
4th rowCan't Help Falling In Love
5th rowHold On
ValueCountFrequency (%)
19536
 
5.1%
the 9421
 
2.5%
you 4270
 
1.1%
me 3695
 
1.0%
a 3676
 
1.0%
of 3588
 
0.9%
i 3384
 
0.9%
vivo 3157
 
0.8%
in 3121
 
0.8%
remix 2966
 
0.8%
Other values (50550) 326490
85.2%
2023-09-21T18:01:40.076935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
269836
 
13.2%
e 173975
 
8.5%
a 135500
 
6.6%
o 121669
 
6.0%
i 108762
 
5.3%
n 93383
 
4.6%
r 91562
 
4.5%
t 81371
 
4.0%
s 67390
 
3.3%
l 62825
 
3.1%
Other values (2407) 831755
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1314294
64.5%
Uppercase Letter 340214
 
16.7%
Space Separator 269836
 
13.2%
Other Punctuation 29912
 
1.5%
Other Letter 23163
 
1.1%
Decimal Number 21098
 
1.0%
Dash Punctuation 17746
 
0.9%
Open Punctuation 9981
 
0.5%
Close Punctuation 9978
 
0.5%
Modifier Letter 616
 
< 0.1%
Other values (13) 1190
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
486
 
2.1%
431
 
1.9%
342
 
1.5%
305
 
1.3%
282
 
1.2%
236
 
1.0%
228
 
1.0%
219
 
0.9%
216
 
0.9%
216
 
0.9%
Other values (2056) 20202
87.2%
Lowercase Letter
ValueCountFrequency (%)
e 173975
13.2%
a 135500
10.3%
o 121669
 
9.3%
i 108762
 
8.3%
n 93383
 
7.1%
r 91562
 
7.0%
t 81371
 
6.2%
s 67390
 
5.1%
l 62825
 
4.8%
u 47409
 
3.6%
Other values (135) 330448
25.1%
Uppercase Letter
ValueCountFrequency (%)
S 27641
 
8.1%
T 25226
 
7.4%
M 24404
 
7.2%
A 24146
 
7.1%
L 18681
 
5.5%
C 18030
 
5.3%
R 17691
 
5.2%
D 17341
 
5.1%
B 16720
 
4.9%
I 15089
 
4.4%
Other values (84) 135245
39.8%
Other Punctuation
ValueCountFrequency (%)
. 7238
24.2%
' 6594
22.0%
, 4452
14.9%
" 3475
11.6%
/ 2363
 
7.9%
: 1896
 
6.3%
& 1460
 
4.9%
! 1108
 
3.7%
? 776
 
2.6%
169
 
0.6%
Other values (11) 381
 
1.3%
Nonspacing Mark
ValueCountFrequency (%)
́ 50
36.0%
26
18.7%
̃ 14
 
10.1%
̧ 12
 
8.6%
̈ 9
 
6.5%
̂ 7
 
5.0%
5
 
3.6%
̆ 5
 
3.6%
̊ 3
 
2.2%
2
 
1.4%
Other values (5) 6
 
4.3%
Decimal Number
ValueCountFrequency (%)
0 4852
23.0%
2 4732
22.4%
1 3847
18.2%
9 2010
9.5%
3 1076
 
5.1%
4 1069
 
5.1%
5 1023
 
4.8%
8 865
 
4.1%
7 839
 
4.0%
6 785
 
3.7%
Math Symbol
ValueCountFrequency (%)
+ 89
36.3%
~ 52
21.2%
| 51
20.8%
= 20
 
8.2%
> 16
 
6.5%
< 12
 
4.9%
2
 
0.8%
1
 
0.4%
1
 
0.4%
× 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 9508
95.3%
[ 354
 
3.5%
76
 
0.8%
35
 
0.4%
3
 
< 0.1%
2
 
< 0.1%
1
 
< 0.1%
{ 1
 
< 0.1%
1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
° 11
28.9%
11
28.9%
8
21.1%
2
 
5.3%
® 2
 
5.3%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Close Punctuation
ValueCountFrequency (%)
) 9507
95.3%
] 353
 
3.5%
76
 
0.8%
35
 
0.4%
3
 
< 0.1%
2
 
< 0.1%
1
 
< 0.1%
} 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 17596
99.2%
112
 
0.6%
26
 
0.1%
10
 
0.1%
2
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 62
80.5%
` 13
 
16.9%
˙ 1
 
1.3%
^ 1
 
1.3%
Final Punctuation
ValueCountFrequency (%)
405
88.2%
45
 
9.8%
» 9
 
2.0%
Initial Punctuation
ValueCountFrequency (%)
68
53.1%
51
39.8%
« 9
 
7.0%
Format
ValueCountFrequency (%)
8
80.0%
1
 
10.0%
 1
 
10.0%
Modifier Letter
ValueCountFrequency (%)
603
97.9%
13
 
2.1%
Currency Symbol
ValueCountFrequency (%)
$ 56
98.2%
1
 
1.8%
Letter Number
ValueCountFrequency (%)
2
66.7%
1
33.3%
Other Number
ValueCountFrequency (%)
½ 2
66.7%
² 1
33.3%
Space Separator
ValueCountFrequency (%)
269836
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 29
100.0%
Private Use
ValueCountFrequency (%)
1
100.0%
Control
ValueCountFrequency (%)
‚ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1643665
80.6%
Common 360200
 
17.7%
Han 13743
 
0.7%
Cyrillic 10810
 
0.5%
Katakana 5060
 
0.2%
Hiragana 4270
 
0.2%
Inherited 138
 
< 0.1%
Hangul 66
 
< 0.1%
Greek 42
 
< 0.1%
Arabic 26
 
< 0.1%
Other values (3) 8
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
342
 
2.5%
236
 
1.7%
228
 
1.7%
207
 
1.5%
202
 
1.5%
201
 
1.5%
158
 
1.1%
131
 
1.0%
121
 
0.9%
114
 
0.8%
Other values (1836) 11803
85.9%
Latin
ValueCountFrequency (%)
e 173975
 
10.6%
a 135500
 
8.2%
o 121669
 
7.4%
i 108762
 
6.6%
n 93383
 
5.7%
r 91562
 
5.6%
t 81371
 
5.0%
s 67390
 
4.1%
l 62825
 
3.8%
u 47409
 
2.9%
Other values (144) 659819
40.1%
Common
ValueCountFrequency (%)
269836
74.9%
- 17596
 
4.9%
( 9508
 
2.6%
) 9507
 
2.6%
. 7238
 
2.0%
' 6594
 
1.8%
0 4852
 
1.3%
2 4732
 
1.3%
, 4452
 
1.2%
1 3847
 
1.1%
Other values (82) 22038
 
6.1%
Katakana
ValueCountFrequency (%)
431
 
8.5%
305
 
6.0%
219
 
4.3%
216
 
4.3%
204
 
4.0%
165
 
3.3%
147
 
2.9%
141
 
2.8%
133
 
2.6%
132
 
2.6%
Other values (69) 2967
58.6%
Hiragana
ValueCountFrequency (%)
486
 
11.4%
282
 
6.6%
216
 
5.1%
163
 
3.8%
134
 
3.1%
132
 
3.1%
129
 
3.0%
122
 
2.9%
118
 
2.8%
111
 
2.6%
Other values (63) 2377
55.7%
Cyrillic
ValueCountFrequency (%)
а 947
 
8.8%
о 899
 
8.3%
е 822
 
7.6%
и 649
 
6.0%
н 607
 
5.6%
р 539
 
5.0%
т 498
 
4.6%
л 460
 
4.3%
с 456
 
4.2%
к 393
 
3.6%
Other values (53) 4540
42.0%
Hangul
ValueCountFrequency (%)
3
 
4.5%
3
 
4.5%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (38) 43
65.2%
Greek
ValueCountFrequency (%)
ο 4
 
9.5%
Ψ 3
 
7.1%
τ 3
 
7.1%
ι 3
 
7.1%
Χ 3
 
7.1%
Ξ 3
 
7.1%
Σ 3
 
7.1%
φ 2
 
4.8%
ε 2
 
4.8%
Δ 1
 
2.4%
Other values (15) 15
35.7%
Arabic
ValueCountFrequency (%)
د 4
15.4%
ر 3
11.5%
م 3
11.5%
ع 2
 
7.7%
ن 2
 
7.7%
ا 2
 
7.7%
ب 1
 
3.8%
ه 1
 
3.8%
ق 1
 
3.8%
س 1
 
3.8%
Other values (6) 6
23.1%
Inherited
ValueCountFrequency (%)
́ 50
36.2%
26
18.8%
̃ 14
 
10.1%
̧ 12
 
8.7%
̈ 9
 
6.5%
̂ 7
 
5.1%
5
 
3.6%
̆ 5
 
3.6%
̊ 3
 
2.2%
2
 
1.4%
Other values (4) 5
 
3.6%
Thai
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Devanagari
ValueCountFrequency (%)
2
100.0%
Unknown
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1987940
97.5%
None 14501
 
0.7%
CJK 13729
 
0.7%
Cyrillic 10810
 
0.5%
Katakana 5832
 
0.3%
Hiragana 4301
 
0.2%
Punctuation 653
 
< 0.1%
Diacriticals 104
 
< 0.1%
Hangul 66
 
< 0.1%
Arabic 26
 
< 0.1%
Other values (15) 66
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
269836
 
13.6%
e 173975
 
8.8%
a 135500
 
6.8%
o 121669
 
6.1%
i 108762
 
5.5%
n 93383
 
4.7%
r 91562
 
4.6%
t 81371
 
4.1%
s 67390
 
3.4%
l 62825
 
3.2%
Other values (84) 781667
39.3%
None
ValueCountFrequency (%)
é 1538
 
10.6%
ã 1400
 
9.7%
á 1110
 
7.7%
ó 1095
 
7.6%
í 894
 
6.2%
ç 842
 
5.8%
ä 662
 
4.6%
ê 604
 
4.2%
ı 588
 
4.1%
ü 574
 
4.0%
Other values (137) 5194
35.8%
Cyrillic
ValueCountFrequency (%)
а 947
 
8.8%
о 899
 
8.3%
е 822
 
7.6%
и 649
 
6.0%
н 607
 
5.6%
р 539
 
5.0%
т 498
 
4.6%
л 460
 
4.3%
с 456
 
4.2%
к 393
 
3.6%
Other values (53) 4540
42.0%
Katakana
ValueCountFrequency (%)
603
 
10.3%
431
 
7.4%
305
 
5.2%
219
 
3.8%
216
 
3.7%
204
 
3.5%
169
 
2.9%
165
 
2.8%
147
 
2.5%
141
 
2.4%
Other values (71) 3232
55.4%
Hiragana
ValueCountFrequency (%)
486
 
11.3%
282
 
6.6%
216
 
5.0%
163
 
3.8%
134
 
3.1%
132
 
3.1%
129
 
3.0%
122
 
2.8%
118
 
2.7%
111
 
2.6%
Other values (65) 2408
56.0%
Punctuation
ValueCountFrequency (%)
405
62.0%
68
 
10.4%
51
 
7.8%
45
 
6.9%
36
 
5.5%
26
 
4.0%
10
 
1.5%
8
 
1.2%
2
 
0.3%
1
 
0.2%
CJK
ValueCountFrequency (%)
342
 
2.5%
236
 
1.7%
228
 
1.7%
207
 
1.5%
202
 
1.5%
201
 
1.5%
158
 
1.2%
131
 
1.0%
121
 
0.9%
114
 
0.8%
Other values (1834) 11789
85.9%
Diacriticals
ValueCountFrequency (%)
́ 50
48.1%
̃ 14
 
13.5%
̧ 12
 
11.5%
̈ 9
 
8.7%
̂ 7
 
6.7%
̆ 5
 
4.8%
̊ 3
 
2.9%
̀ 2
 
1.9%
̌ 1
 
1.0%
̇ 1
 
1.0%
Misc Symbols
ValueCountFrequency (%)
11
55.0%
8
40.0%
1
 
5.0%
IPA Ext
ValueCountFrequency (%)
ə 10
100.0%
Arabic
ValueCountFrequency (%)
د 4
15.4%
ر 3
11.5%
م 3
11.5%
ع 2
 
7.7%
ن 2
 
7.7%
ا 2
 
7.7%
ب 1
 
3.8%
ه 1
 
3.8%
ق 1
 
3.8%
س 1
 
3.8%
Other values (6) 6
23.1%
Hangul
ValueCountFrequency (%)
3
 
4.5%
3
 
4.5%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (38) 43
65.2%
Latin Ext Additional
ValueCountFrequency (%)
3
23.1%
2
15.4%
2
15.4%
2
15.4%
2
15.4%
2
15.4%
Devanagari
ValueCountFrequency (%)
2
100.0%
Dingbats
ValueCountFrequency (%)
2
100.0%
VS
ValueCountFrequency (%)
2
100.0%
Number Forms
ValueCountFrequency (%)
2
100.0%
Arrows
ValueCountFrequency (%)
2
100.0%
Geometric Shapes
ValueCountFrequency (%)
1
50.0%
1
50.0%
Misc Technical
ValueCountFrequency (%)
1
100.0%
Thai
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%
PUA
ValueCountFrequency (%)
1
100.0%
Math Operators
ValueCountFrequency (%)
1
50.0%
1
50.0%
Modifier Letters
ValueCountFrequency (%)
˙ 1
100.0%

popularidade
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.314802
Minimum0
Maximum100
Zeros15830
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:40.359424image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median35
Q350
95-th percentile69
Maximum100
Range100
Interquartile range (IQR)33

Descriptive statistics

Standard deviation22.278127
Coefficient of variation (CV)0.66871558
Kurtosis-0.92331704
Mean33.314802
Median Absolute Deviation (MAD)16
Skewness0.042696446
Sum3780164
Variance496.31496
MonotonicityNot monotonic
2023-09-21T18:01:40.595147image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15830
 
14.0%
22 2347
 
2.1%
21 2331
 
2.1%
44 2286
 
2.0%
23 2114
 
1.9%
1 2114
 
1.9%
20 2093
 
1.8%
43 2070
 
1.8%
45 2001
 
1.8%
41 1993
 
1.8%
Other values (91) 78289
69.0%
ValueCountFrequency (%)
0 15830
14.0%
1 2114
 
1.9%
2 1023
 
0.9%
3 570
 
0.5%
4 377
 
0.3%
5 592
 
0.5%
6 419
 
0.4%
7 455
 
0.4%
8 538
 
0.5%
9 522
 
0.5%
ValueCountFrequency (%)
100 2
 
< 0.1%
99 1
 
< 0.1%
98 7
< 0.1%
97 8
< 0.1%
96 7
< 0.1%
95 5
< 0.1%
94 7
< 0.1%
93 12
< 0.1%
92 9
< 0.1%
91 10
< 0.1%

duracao_ms
Real number (ℝ)

Distinct50696
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228094.46
Minimum8586
Maximum5237295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:40.848887image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum8586
5-th percentile117134.05
Q1174182.75
median213004.5
Q3261600
95-th percentile387168.3
Maximum5237295
Range5228709
Interquartile range (IQR)87417.25

Descriptive statistics

Standard deviation106436.94
Coefficient of variation (CV)0.46663537
Kurtosis338.52713
Mean228094.46
Median Absolute Deviation (MAD)42694.5
Skewness10.814632
Sum2.5881422 × 1010
Variance1.1328823 × 1010
MonotonicityNot monotonic
2023-09-21T18:01:41.096445image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162897 146
 
0.1%
180000 104
 
0.1%
192000 91
 
0.1%
240000 84
 
0.1%
118840 74
 
0.1%
227520 71
 
0.1%
172342 70
 
0.1%
131733 69
 
0.1%
243057 66
 
0.1%
175986 63
 
0.1%
Other values (50686) 112630
99.3%
ValueCountFrequency (%)
8586 1
< 0.1%
13386 1
< 0.1%
15800 1
< 0.1%
17453 1
< 0.1%
17826 2
< 0.1%
21120 1
< 0.1%
21240 1
< 0.1%
22266 1
< 0.1%
23506 2
< 0.1%
24000 1
< 0.1%
ValueCountFrequency (%)
5237295 1
< 0.1%
4789026 1
< 0.1%
4730302 1
< 0.1%
4563897 1
< 0.1%
4447520 1
< 0.1%
4339826 1
< 0.1%
4334721 1
< 0.1%
4246206 1
< 0.1%
4120258 1
< 0.1%
3876276 2
< 0.1%

explicito
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size997.3 KiB
False
103756 
True
 
9712
ValueCountFrequency (%)
False 103756
91.4%
True 9712
 
8.6%
2023-09-21T18:01:41.328210image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

dancabilidade
Real number (ℝ)

Distinct1174
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56696319
Minimum0
Maximum0.985
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:41.550989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.456
median0.58
Q30.695
95-th percentile0.824
Maximum0.985
Range0.985
Interquartile range (IQR)0.239

Descriptive statistics

Standard deviation0.17341564
Coefficient of variation (CV)0.30586755
Kurtosis-0.1807179
Mean0.56696319
Median Absolute Deviation (MAD)0.119
Skewness-0.40016193
Sum64332.179
Variance0.030072985
MonotonicityNot monotonic
2023-09-21T18:01:41.815792image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.647 430
 
0.4%
0.609 355
 
0.3%
0.579 346
 
0.3%
0.602 331
 
0.3%
0.685 331
 
0.3%
0.689 313
 
0.3%
0.524 313
 
0.3%
0.598 312
 
0.3%
0.607 306
 
0.3%
0.631 305
 
0.3%
Other values (1164) 110126
97.1%
ValueCountFrequency (%)
0 157
0.1%
0.0513 1
 
< 0.1%
0.0532 1
 
< 0.1%
0.0545 1
 
< 0.1%
0.0548 1
 
< 0.1%
0.055 1
 
< 0.1%
0.0555 1
 
< 0.1%
0.0558 1
 
< 0.1%
0.0562 1
 
< 0.1%
0.0565 2
 
< 0.1%
ValueCountFrequency (%)
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 2
< 0.1%
0.98 2
< 0.1%
0.979 2
< 0.1%
0.978 3
< 0.1%
0.977 1
 
< 0.1%
0.976 4
< 0.1%

energia
Real number (ℝ)

HIGH CORRELATION 

Distinct2083
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64207052
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:42.128229image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.156
Q10.473
median0.685
Q30.854
95-th percentile0.969
Maximum1
Range1
Interquartile range (IQR)0.381

Descriptive statistics

Standard deviation0.25110026
Coefficient of variation (CV)0.39107895
Kurtosis-0.52039204
Mean0.64207052
Median Absolute Deviation (MAD)0.186
Skewness-0.59831161
Sum72854.457
Variance0.063051341
MonotonicityNot monotonic
2023-09-21T18:01:42.380277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.876 318
 
0.3%
0.937 269
 
0.2%
0.931 261
 
0.2%
0.886 256
 
0.2%
0.948 254
 
0.2%
0.961 253
 
0.2%
0.858 253
 
0.2%
0.801 253
 
0.2%
0.92 239
 
0.2%
0.979 237
 
0.2%
Other values (2073) 110875
97.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1.95 × 10-51
 
< 0.1%
2.01 × 10-513
 
< 0.1%
2.02 × 10-54
 
< 0.1%
2.03 × 10-534
< 0.1%
2.82 × 10-51
 
< 0.1%
3.05 × 10-51
 
< 0.1%
3.61 × 10-51
 
< 0.1%
4.28 × 10-53
 
< 0.1%
5.9 × 10-52
 
< 0.1%
ValueCountFrequency (%)
1 28
 
< 0.1%
0.999 100
0.1%
0.998 149
0.1%
0.997 164
0.1%
0.996 158
0.1%
0.995 229
0.2%
0.994 172
0.2%
0.993 183
0.2%
0.992 161
0.1%
0.991 200
0.2%

chave
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3088977
Minimum0
Maximum11
Zeros13000
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:42.609278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5601779
Coefficient of variation (CV)0.67060587
Kurtosis-1.2766873
Mean5.3088977
Median Absolute Deviation (MAD)3
Skewness-0.0084148854
Sum602390
Variance12.674867
MonotonicityNot monotonic
2023-09-21T18:01:42.814274image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 13196
11.6%
0 13000
11.5%
2 11590
10.2%
9 11258
9.9%
1 10732
9.5%
5 9320
8.2%
11 9242
8.1%
4 8966
7.9%
6 7880
6.9%
10 7418
6.5%
Other values (2) 10866
9.6%
ValueCountFrequency (%)
0 13000
11.5%
1 10732
9.5%
2 11590
10.2%
3 3546
 
3.1%
4 8966
7.9%
5 9320
8.2%
6 7880
6.9%
7 13196
11.6%
8 7320
6.5%
9 11258
9.9%
ValueCountFrequency (%)
11 9242
8.1%
10 7418
6.5%
9 11258
9.9%
8 7320
6.5%
7 13196
11.6%
6 7880
6.9%
5 9320
8.2%
4 8966
7.9%
3 3546
 
3.1%
2 11590
10.2%

volume
Real number (ℝ)

HIGH CORRELATION 

Distinct19480
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.2445195
Minimum-49.531
Maximum4.532
Zeros0
Zeros (%)0.0%
Negative113378
Negative (%)99.9%
Memory size1.7 MiB
2023-09-21T18:01:43.043373image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-49.531
5-th percentile-17.999
Q1-9.99925
median-6.998
Q3-5.001
95-th percentile-2.973
Maximum4.532
Range54.063
Interquartile range (IQR)4.99825

Descriptive statistics

Standard deviation5.012435
Coefficient of variation (CV)-0.60797176
Kurtosis5.9591493
Mean-8.2445195
Median Absolute Deviation (MAD)2.338
Skewness-2.0130362
Sum-935489.14
Variance25.124505
MonotonicityNot monotonic
2023-09-21T18:01:43.299827image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.662 175
 
0.2%
-4.457 85
 
0.1%
-9.336 84
 
0.1%
-7.57 76
 
0.1%
-8.871 74
 
0.1%
-4.034 72
 
0.1%
-3.725 71
 
0.1%
-4.324 70
 
0.1%
-12.472 64
 
0.1%
-6.196 63
 
0.1%
Other values (19470) 112634
99.3%
ValueCountFrequency (%)
-49.531 1
 
< 0.1%
-49.307 1
 
< 0.1%
-46.591 1
 
< 0.1%
-46.251 1
 
< 0.1%
-43.957 1
 
< 0.1%
-43.943 1
 
< 0.1%
-43.714 1
 
< 0.1%
-43.504 1
 
< 0.1%
-43.303 1
 
< 0.1%
-43.046 3
< 0.1%
ValueCountFrequency (%)
4.532 1
< 0.1%
3.156 1
< 0.1%
2.574 1
< 0.1%
1.864 1
< 0.1%
1.821 1
< 0.1%
1.795 1
< 0.1%
1.7 1
< 0.1%
1.682 1
< 0.1%
1.673 1
< 0.1%
1.416 1
< 0.1%

modo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1
72393 
0
41075 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113468
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 72393
63.8%
0 41075
36.2%

Length

2023-09-21T18:01:43.531834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-21T18:01:43.722898image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 72393
63.8%
0 41075
36.2%

Most occurring characters

ValueCountFrequency (%)
1 72393
63.8%
0 41075
36.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113468
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 72393
63.8%
0 41075
36.2%

Most occurring scripts

ValueCountFrequency (%)
Common 113468
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 72393
63.8%
0 41075
36.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 72393
63.8%
0 41075
36.2%

fala
Real number (ℝ)

Distinct1489
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.084656995
Minimum0
Maximum0.965
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:43.923958image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0282
Q10.0359
median0.0489
Q30.0845
95-th percentile0.268
Maximum0.965
Range0.965
Interquartile range (IQR)0.0486

Descriptive statistics

Standard deviation0.10575962
Coefficient of variation (CV)1.2492721
Kurtosis28.812923
Mean0.084656995
Median Absolute Deviation (MAD)0.0165
Skewness4.6468184
Sum9605.8599
Variance0.011185098
MonotonicityNot monotonic
2023-09-21T18:01:44.186277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0323 398
 
0.4%
0.0324 374
 
0.3%
0.0322 373
 
0.3%
0.0328 361
 
0.3%
0.0295 356
 
0.3%
0.0321 350
 
0.3%
0.033 347
 
0.3%
0.0367 343
 
0.3%
0.0326 340
 
0.3%
0.0306 331
 
0.3%
Other values (1479) 109895
96.9%
ValueCountFrequency (%)
0 157
0.1%
0.0221 3
 
< 0.1%
0.0222 1
 
< 0.1%
0.0223 3
 
< 0.1%
0.0225 2
 
< 0.1%
0.0226 2
 
< 0.1%
0.0227 3
 
< 0.1%
0.0228 5
 
< 0.1%
0.0229 1
 
< 0.1%
0.023 9
 
< 0.1%
ValueCountFrequency (%)
0.965 1
 
< 0.1%
0.963 2
 
< 0.1%
0.962 6
< 0.1%
0.961 2
 
< 0.1%
0.96 3
 
< 0.1%
0.959 6
< 0.1%
0.958 6
< 0.1%
0.957 8
< 0.1%
0.956 7
< 0.1%
0.955 11
< 0.1%

acustica
Real number (ℝ)

HIGH CORRELATION 

Distinct5061
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31413804
Minimum0
Maximum0.996
Zeros39
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:44.448347image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000145
Q10.0168
median0.168
Q30.596
95-th percentile0.947
Maximum0.996
Range0.996
Interquartile range (IQR)0.5792

Descriptive statistics

Standard deviation0.33196225
Coefficient of variation (CV)1.0567401
Kurtosis-0.94441297
Mean0.31413804
Median Absolute Deviation (MAD)0.16652
Skewness0.72979809
Sum35644.615
Variance0.11019893
MonotonicityNot monotonic
2023-09-21T18:01:44.709283image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 292
 
0.3%
0.993 260
 
0.2%
0.994 259
 
0.2%
0.992 241
 
0.2%
0.991 215
 
0.2%
0.881 204
 
0.2%
0.131 201
 
0.2%
0.108 193
 
0.2%
0.107 188
 
0.2%
0.99 184
 
0.2%
Other values (5051) 111231
98.0%
ValueCountFrequency (%)
0 39
< 0.1%
1 × 10-61
 
< 0.1%
1.01 × 10-64
 
< 0.1%
1.02 × 10-61
 
< 0.1%
1.03 × 10-62
 
< 0.1%
1.04 × 10-64
 
< 0.1%
1.06 × 10-65
 
< 0.1%
1.07 × 10-64
 
< 0.1%
1.08 × 10-62
 
< 0.1%
1.09 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 102
 
0.1%
0.995 292
0.3%
0.994 259
0.2%
0.993 260
0.2%
0.992 241
0.2%
0.991 215
0.2%
0.99 184
0.2%
0.989 172
0.2%
0.988 148
0.1%
0.987 154
0.1%

instrumentalidade
Real number (ℝ)

ZEROS 

Distinct5346
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15568918
Minimum0
Maximum1
Zeros38621
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:44.969288image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.125 × 10-5
Q30.0486
95-th percentile0.904
Maximum1
Range1
Interquartile range (IQR)0.0486

Descriptive statistics

Standard deviation0.30922188
Coefficient of variation (CV)1.9861488
Kurtosis1.2837437
Mean0.15568918
Median Absolute Deviation (MAD)4.125 × 10-5
Skewness1.7379307
Sum17665.74
Variance0.095618172
MonotonicityNot monotonic
2023-09-21T18:01:45.222277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38621
34.0%
3.59 × 10-5166
 
0.1%
0.905 122
 
0.1%
0.895 121
 
0.1%
0.934 120
 
0.1%
0.922 118
 
0.1%
0.000141 115
 
0.1%
0.913 114
 
0.1%
0.911 113
 
0.1%
0.9 112
 
0.1%
Other values (5336) 73746
65.0%
ValueCountFrequency (%)
0 38621
34.0%
1 × 10-632
 
< 0.1%
1.01 × 10-646
 
< 0.1%
1.02 × 10-636
 
< 0.1%
1.03 × 10-634
 
< 0.1%
1.04 × 10-650
 
< 0.1%
1.05 × 10-639
 
< 0.1%
1.06 × 10-649
 
< 0.1%
1.07 × 10-656
 
< 0.1%
1.08 × 10-647
 
< 0.1%
ValueCountFrequency (%)
1 13
< 0.1%
0.999 22
< 0.1%
0.998 6
 
< 0.1%
0.997 11
< 0.1%
0.996 4
 
< 0.1%
0.995 15
< 0.1%
0.994 4
 
< 0.1%
0.993 9
< 0.1%
0.992 11
< 0.1%
0.991 12
< 0.1%

vivacidade
Real number (ℝ)

Distinct1722
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2136349
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:45.473394image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0606
Q10.098
median0.132
Q30.273
95-th percentile0.681
Maximum1
Range1
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.19047906
Coefficient of variation (CV)0.89161022
Kurtosis4.3753205
Mean0.2136349
Median Absolute Deviation (MAD)0.051
Skewness2.1053263
Sum24240.725
Variance0.036282273
MonotonicityNot monotonic
2023-09-21T18:01:45.748618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.108 1344
 
1.2%
0.111 1314
 
1.2%
0.109 1192
 
1.1%
0.11 1172
 
1.0%
0.105 1110
 
1.0%
0.107 1096
 
1.0%
0.103 1091
 
1.0%
0.106 1061
 
0.9%
0.112 1054
 
0.9%
0.113 999
 
0.9%
Other values (1712) 102035
89.9%
ValueCountFrequency (%)
0 2
< 0.1%
0.00925 1
< 0.1%
0.00986 1
< 0.1%
0.0112 1
< 0.1%
0.0114 1
< 0.1%
0.0116 1
< 0.1%
0.0118 1
< 0.1%
0.0133 1
< 0.1%
0.0136 1
< 0.1%
0.0137 1
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.997 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 3
 
< 0.1%
0.993 2
 
< 0.1%
0.992 9
< 0.1%
0.991 4
 
< 0.1%
0.99 11
< 0.1%
0.989 17
< 0.1%
0.988 17
< 0.1%

valencia
Real number (ℝ)

Distinct1790
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47424942
Minimum0
Maximum0.995
Zeros176
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:46.025623image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.071
Q10.26
median0.464
Q30.683
95-th percentile0.911
Maximum0.995
Range0.995
Interquartile range (IQR)0.423

Descriptive statistics

Standard deviation0.2592195
Coefficient of variation (CV)0.54658898
Kurtosis-1.0272867
Mean0.47424942
Median Absolute Deviation (MAD)0.212
Skewness0.11456871
Sum53812.133
Variance0.067194751
MonotonicityNot monotonic
2023-09-21T18:01:46.285721image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 299
 
0.3%
0.304 245
 
0.2%
0.717 231
 
0.2%
0.962 230
 
0.2%
0.324 223
 
0.2%
0.963 214
 
0.2%
0.365 205
 
0.2%
0.55 205
 
0.2%
0.949 203
 
0.2%
0.202 199
 
0.2%
Other values (1780) 111214
98.0%
ValueCountFrequency (%)
0 176
0.2%
1 × 10-5129
0.1%
0.000322 1
 
< 0.1%
0.000378 1
 
< 0.1%
0.000667 1
 
< 0.1%
0.000673 1
 
< 0.1%
0.000755 1
 
< 0.1%
0.000781 1
 
< 0.1%
0.00084 1
 
< 0.1%
0.000885 1
 
< 0.1%
ValueCountFrequency (%)
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.993 3
< 0.1%
0.992 4
< 0.1%
0.991 3
< 0.1%
0.99 1
 
< 0.1%
0.989 1
 
< 0.1%
0.988 4
< 0.1%
0.987 2
< 0.1%
0.986 1
 
< 0.1%

tempo
Real number (ℝ)

Distinct45652
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.17905
Minimum0
Maximum243.372
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:46.540204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.408
Q199.303
median122.022
Q3140.076
95-th percentile175.06665
Maximum243.372
Range243.372
Interquartile range (IQR)40.773

Descriptive statistics

Standard deviation29.96978
Coefficient of variation (CV)0.24529393
Kurtosis-0.10697597
Mean122.17905
Median Absolute Deviation (MAD)21.6725
Skewness0.23074103
Sum13863413
Variance898.18774
MonotonicityNot monotonic
2023-09-21T18:01:46.785723image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 157
 
0.1%
151.925 146
 
0.1%
95.004 89
 
0.1%
130.594 74
 
0.1%
87.925 71
 
0.1%
125.004 70
 
0.1%
92.988 70
 
0.1%
76.783 68
 
0.1%
77.321 67
 
0.1%
90.04 62
 
0.1%
Other values (45642) 112594
99.2%
ValueCountFrequency (%)
0 157
0.1%
30.2 1
 
< 0.1%
30.322 1
 
< 0.1%
31.834 1
 
< 0.1%
34.262 1
 
< 0.1%
34.821 1
 
< 0.1%
35.392 1
 
< 0.1%
35.79 1
 
< 0.1%
35.862 1
 
< 0.1%
35.928 1
 
< 0.1%
ValueCountFrequency (%)
243.372 1
 
< 0.1%
222.605 1
 
< 0.1%
220.525 1
 
< 0.1%
220.084 1
 
< 0.1%
220.081 3
< 0.1%
220.039 1
 
< 0.1%
219.971 1
 
< 0.1%
219.693 1
 
< 0.1%
219.571 1
 
< 0.1%
218.879 1
 
< 0.1%

assinatura_hora
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
4
101406 
3
 
9128
5
 
1805
1
 
966
0
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113468
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 101406
89.4%
3 9128
 
8.0%
5 1805
 
1.6%
1 966
 
0.9%
0 163
 
0.1%

Length

2023-09-21T18:01:47.001581image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-21T18:01:47.232971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4 101406
89.4%
3 9128
 
8.0%
5 1805
 
1.6%
1 966
 
0.9%
0 163
 
0.1%

Most occurring characters

ValueCountFrequency (%)
4 101406
89.4%
3 9128
 
8.0%
5 1805
 
1.6%
1 966
 
0.9%
0 163
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113468
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 101406
89.4%
3 9128
 
8.0%
5 1805
 
1.6%
1 966
 
0.9%
0 163
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 113468
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 101406
89.4%
3 9128
 
8.0%
5 1805
 
1.6%
1 966
 
0.9%
0 163
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 101406
89.4%
3 9128
 
8.0%
5 1805
 
1.6%
1 966
 
0.9%
0 163
 
0.1%
Distinct112
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2023-09-21T18:01:47.571577image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length17
Median length13
Mean length7.3064476
Min length3

Characters and Unicode

Total characters829048
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowacústico
2nd rowacústico
3rd rowacústico
4th rowacústico
5th rowacústico
ValueCountFrequency (%)
infantil 1984
 
1.7%
eletrônica 1922
 
1.6%
techno 1000
 
0.9%
reggaeton 1000
 
0.9%
songwriter 1000
 
0.9%
dormir 1000
 
0.9%
synth-pop 1000
 
0.9%
sueca 1000
 
0.9%
espanhola 1000
 
0.9%
britânico 1000
 
0.9%
Other values (106) 105552
89.9%
2023-09-21T18:01:48.187433image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 81439
 
9.8%
e 75564
 
9.1%
o 70762
 
8.5%
r 58746
 
7.1%
i 52721
 
6.4%
n 52641
 
6.3%
t 47823
 
5.8%
l 38743
 
4.7%
c 38609
 
4.7%
p 37910
 
4.6%
Other values (22) 274090
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 793136
95.7%
Dash Punctuation 31922
 
3.9%
Space Separator 3990
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 81439
 
10.3%
e 75564
 
9.5%
o 70762
 
8.9%
r 58746
 
7.4%
i 52721
 
6.6%
n 52641
 
6.6%
t 47823
 
6.0%
l 38743
 
4.9%
c 38609
 
4.9%
p 37910
 
4.8%
Other values (20) 238178
30.0%
Dash Punctuation
ValueCountFrequency (%)
- 31922
100.0%
Space Separator
ValueCountFrequency (%)
3990
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 793136
95.7%
Common 35912
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 81439
 
10.3%
e 75564
 
9.5%
o 70762
 
8.9%
r 58746
 
7.4%
i 52721
 
6.6%
n 52641
 
6.6%
t 47823
 
6.0%
l 38743
 
4.9%
c 38609
 
4.9%
p 37910
 
4.8%
Other values (20) 238178
30.0%
Common
ValueCountFrequency (%)
- 31922
88.9%
3990
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 822165
99.2%
None 6883
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 81439
 
9.9%
e 75564
 
9.2%
o 70762
 
8.6%
r 58746
 
7.1%
i 52721
 
6.4%
n 52641
 
6.4%
t 47823
 
5.8%
l 38743
 
4.7%
c 38609
 
4.7%
p 37910
 
4.6%
Other values (17) 267207
32.5%
None
ValueCountFrequency (%)
ã 1962
28.5%
ô 1922
27.9%
ú 1000
14.5%
â 1000
14.5%
ç 999
14.5%

Interactions

2023-09-21T18:01:29.554843image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:00:58.705895image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:01.582796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:05.119384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:07.919430image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:10.861933image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:13.476056image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:16.125361image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:18.713771image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:21.623816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:24.219313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:26.852060image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:29.787014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:00:59.088865image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:01.822451image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:05.349371image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:08.141072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:11.069936image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:13.688985image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:16.358196image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:18.928835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:21.836506image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:24.429313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:27.068058image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:30.004936image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:00:59.317799image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:02.036937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:05.595372image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:08.362067image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:11.299195image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:13.906987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:16.579877image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:19.429117image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:22.053171image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:24.653313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:27.298243image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:30.217255image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:00:59.538799image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:02.362823image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:05.848579image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:08.909137image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:11.525193image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:14.154457image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:16.788592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:19.646117image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:22.272683image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:24.869315image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:27.506314image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:30.435183image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:00:59.765795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:02.895820image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:06.102584image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:09.119858image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:11.747193image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:14.366460image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:16.989914image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:19.875118image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:22.481915image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:25.081582image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:27.724322image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:30.645253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:00:59.976864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:03.326819image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:06.326898image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:09.329858image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:11.954120image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:14.573524image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:17.212742image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:20.104592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:22.687209image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:25.305587image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:27.946393image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:30.861185image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:00.197819image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:03.659820image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:06.553571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:09.543391image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:12.175257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:14.790051image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:17.419991image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:20.320599image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:22.906081image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:25.526654image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:28.196916image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:31.076874image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:00.476304image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:03.965821image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:06.784738image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:09.762391image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:12.393988image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:15.006048image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:17.636702image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:20.533675image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:23.121383image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:25.741588image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:28.424915image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:31.300935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:00.690561image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:04.199443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:06.993168image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:09.978473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:12.607986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:15.240054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:17.839775image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:20.753597image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:23.334180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:25.961583image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:28.636844image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:31.523940image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:00.917225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:04.448371image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:07.235879image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:10.207934image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:12.839056image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:15.476252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:18.068565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:20.972597image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:23.570913image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:26.212057image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:28.874930image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:32.112252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:01.135114image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:04.673370image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:07.471948image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:10.416935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:13.051986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:15.693251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:18.280570image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:21.186592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:23.774805image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:26.420059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:29.102915image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:32.343259image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:01.369473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:04.901376image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:07.702025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:10.645932image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:13.266029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:15.913247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:18.501765image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:21.398593image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:23.987442image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:26.638057image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-21T18:01:29.338927image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-09-21T18:01:48.434918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
popularidadeduracao_msdancabilidadeenergiachavevolumefalaacusticainstrumentalidadevivacidadevalenciatempoexplicitomodoassinatura_hora
popularidade1.0000.0270.025-0.027-0.0030.033-0.0680.011-0.079-0.008-0.0430.0160.0890.0370.045
duracao_ms0.0271.000-0.0980.1030.0140.020-0.129-0.1690.128-0.041-0.1780.0500.0110.0050.036
dancabilidade0.025-0.0981.0000.0360.0350.1090.158-0.036-0.143-0.1450.461-0.0720.1540.0850.280
energia-0.0270.1030.0361.0000.0450.7490.355-0.707-0.0330.1770.2070.2410.1160.0880.161
chave-0.0030.0140.0350.0451.0000.0320.044-0.0380.005-0.0040.0340.0120.0400.2470.021
volume0.0330.0200.1090.7490.0321.0000.232-0.532-0.2880.1120.2200.1940.1070.0450.153
fala-0.068-0.1290.1580.3550.0440.2321.000-0.214-0.0490.0920.0920.1150.3050.0680.085
acustica0.011-0.169-0.036-0.707-0.038-0.532-0.2141.000-0.099-0.042-0.020-0.2170.1010.1010.140
instrumentalidade-0.0790.128-0.143-0.0330.005-0.288-0.049-0.0991.000-0.099-0.320-0.0050.1030.0590.067
vivacidade-0.008-0.041-0.1450.177-0.0040.1120.092-0.042-0.0991.0000.0130.0200.0420.0290.040
valencia-0.043-0.1780.4610.2070.0340.2200.092-0.020-0.3200.0131.0000.0620.0690.0320.111
tempo0.0160.050-0.0720.2410.0120.1940.115-0.217-0.0050.0200.0621.0000.0400.0270.496
explicito0.0890.0110.1540.1160.0400.1070.3050.1010.1030.0420.0690.0401.0000.0370.060
modo0.0370.0050.0850.0880.2470.0450.0680.1010.0590.0290.0320.0270.0371.0000.029
assinatura_hora0.0450.0360.2800.1610.0210.1530.0850.1400.0670.0400.1110.4960.0600.0291.000

Missing values

2023-09-21T18:01:32.850324image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-21T18:01:33.796531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

id_musicaartistasnome_do_albumnome_da_faixapopularidadeduracao_msexplicitodancabilidadeenergiachavevolumemodofalaacusticainstrumentalidadevivacidadevalenciatempoassinatura_horagênero
05SuOikwiRyPMVoIQDJUgSVGen HoshinoComedyComedy73230666False0.6760.46101-6.74600.14300.03220.0000010.35800.715087.9174acústico
14qPNDBW1i3p13qLCt0Ki3ABen WoodwardGhost (Acoustic)Ghost - Acoustic55149610False0.4200.16601-17.23510.07630.92400.0000060.10100.267077.4894acústico
21iJBSr7s7jYXzM8EGcbK5bIngrid Michaelson;ZAYNTo Begin AgainTo Begin Again57210826False0.4380.35900-9.73410.05570.21000.0000000.11700.120076.3324acústico
36lfxq3CG4xtTiEg7opyCyxKina GrannisCrazy Rich Asians (Original Motion Picture Soundtrack)Can't Help Falling In Love71201933False0.2660.05960-18.51510.03630.90500.0000710.13200.1430181.7403acústico
45vjLSffimiIP26QG5WcN2KChord OverstreetHold OnHold On82198853False0.6180.44302-9.68110.05260.46900.0000000.08290.1670119.9494acústico
501MVOl9KtVTNfFiBU9I7dcTyrone WellsDays I Will RememberDays I Will Remember58214240False0.6880.48106-8.80710.10500.28900.0000000.18900.666098.0174acústico
66Vc5wAMmXdKIAM7WUoEb7NA Great Big World;Christina AguileraIs There Anybody Out There?Say Something74229400False0.4070.14702-8.82210.03550.85700.0000030.09130.0765141.2843acústico
71EzrEOXmMH3G43AXT1y7pAJason MrazWe Sing. We Dance. We Steal Things.I'm Yours80242946False0.7030.444011-9.33110.04170.55900.0000000.09730.7120150.9604acústico
80IktbUcnAGrvD03AWnz3Q8Jason Mraz;Colbie CaillatWe Sing. We Dance. We Steal Things.Lucky74189613False0.6250.41400-8.70010.03690.29400.0000000.15100.6690130.0884acústico
97k9GuJYLp2AzqokyEdwEw2Ross CoppermanHungerHunger56205594False0.4420.63201-6.77010.02950.42600.0041900.07350.196078.8994acústico
id_musicaartistasnome_do_albumnome_da_faixapopularidadeduracao_msexplicitodancabilidadeenergiachavevolumemodofalaacusticainstrumentalidadevivacidadevalenciatempoassinatura_horagênero
1139902A4dSiJmbviL56CBupkh6CLucas CervettiFrecuencias Álmicas en 432hz (Solo Piano)Frecuencia Álmica XI - Solo Piano22369049False0.5790.2454-16.35710.03840.970000.9240000.10100.3020112.0113world-music
1139910CE0Y6GM75cbrqao8EOAlWChris TomlinThe Ultimate PlaylistAt The Cross (Love Ran Red)32250629False0.3870.5318-4.78810.02900.003050.0000000.20100.1530146.0034world-music
1139923FjOBB4EyIXHYUtSgrIdY9Jesus CultureRevelation SongsYour Love Never Fails38312566False0.4750.86010-4.72210.04210.006500.0000020.24600.4270113.9494world-music
1139934OkMK49i3NApR1KsAIsTf6Chris TomlinSee The Morning (Special Edition)How Can I Keep From Singing39256026False0.5050.68710-4.37510.02870.084100.0000000.18800.3820104.0833world-music
1139944WbOUe6T0sozC7z5ZJgiAALucas CervettiFrecuencias Álmicas en 432hzFrecuencia Álmica, Pt. 422305454False0.3310.1711-15.66810.03500.920000.0229000.06790.3270132.1473world-music
1139952C3TZjDRiAzdyViavDJ217Rainy Lullaby#mindfulness - Soft Rain for Mindful Meditation, Stress Relief Relaxation MusicSleep My Little Boy21384999False0.1720.2355-16.39310.04220.640000.9280000.08630.0339125.9955world-music
1139961hIz5L4IB9hN3WRYPOCGPwRainy Lullaby#mindfulness - Soft Rain for Mindful Meditation, Stress Relief Relaxation MusicWater Into Light22385000False0.1740.1170-18.31800.04010.994000.9760000.10500.035085.2394world-music
1139976x8ZfSoqDjuNa5SVP5QjvXCesária EvoraBest OfMiss Perfumado22271466False0.6290.3290-10.89500.04200.867000.0000000.08390.7430132.3784world-music
1139982e6sXL2bYv4bSz6VTdnfLsMichael W. SmithChange Your WorldFriends41283893False0.5870.5067-10.88910.02970.381000.0000000.27000.4130135.9604world-music
1139992hETkH7cOfqmz3LqZDHZf5Cesária EvoraMiss PerfumadoBarbincor22241826False0.5260.4871-10.20400.07250.681000.0000000.08930.708079.1984world-music